Driving a vehicle continues to be one of the most hazardous activities that a person can participate in. Vehicle accidents are one of the leading cause of death every year. Damage from accidents amounts to billions of dollars a year. To date, most vehicle accidents are assessed after-the-fact by personal arriving on the scene after the incident. The assessments almost always utilize some type of human interface either to estimate damage or transpose information into a machine readable form. This human interface introduces many biases and uncertainties into the process. These biases and uncertainties then translate into litigation when it is necessary to determine cause for insurance purposes or from a safety standard.
Attempts have been made to take sensor information from in-vehicle sensors and associate this information to external information and factors to reconstruct accidents and/or determine when an accident occurs, but this work is in early stages.
The advent of unmanned aerial vehicles (UAV) with associated sensor arrays has added a new method of monitoring vehicle activity and accident scene surveillance. However, the changing regulatory atmosphere makes reliance on any one type of surveillance method risky from the fact that it may be illegal in the near future. For example, in December 2014, the US Congress is considering that UAVs for commercial unlicensed use can only fly 400 feet in the air and must be in view of the handler. Even more restrictive rulings may apply. For this reason and others, data fusion among several sensor arrays is important to any vehicle or accident scene surveillance system as certain methods may not be allowed in the long run.
An aim of this invention is both to navigate the uncertain regulatory landscape and also take advantage of the array of sensors; sensor delivery vehicles and methods; and statistical and machine learning analysis techniques for accident prediction and accident scene surveillance.